Software Engineer Intern - AI Infra for Scalable ML

DatologyAI; California (MO); On-site; USD 28,000 - 39,000; Full time

Benefits offered by this job

Health benefits

401(k) plan

Unlimited PTO

Paid Parental Leave

Wellness stipend

Learning stipend

Daily lunches

Relocation assistance

Job summary

DatologyAI in San Mateo, CA, is seeking a Software Engineer Intern to join our Infrastructure team for Winter 2027 (January–April 2027). You will design and build systems that power large-scale data curation and model training, gaining hands-on experience with distributed systems and multi-cloud environments.

You’ll own meaningful projects, learn scale, reliability, and efficiency in AI workloads, and collaborate with senior engineers and researchers to push production-ready ML infrastructure

Qualifications

• Pursuing BS, MS, or PhD in Computer Science, Electrical Engineering, or a related field.
• Strong programming skills in Python, Go, or C++.
• Familiar with Linux systems, Docker, Kubernetes, or similar technologies.
• Curious about cloud computing and large-scale distributed systems.

Responsibilities

• Build and improve internal tools that accelerate developer productivity and system reliability.
• Design and prototype components of DatologyAI's distributed training and data infrastructure.
• Contribute to automation, deployment, and observability systems across multi-cloud and on-prem environments.
• Collaborate with engineers and researchers to bring new ML infrastructure capabilities to production.
• Participate in code reviews, technical discussions, and learn best practices for scalable infrastructure development.

Skills

Python

Go

C++

Linux

Docker

Kubernetes

Distributed systems

Cloud computing

Education

BS/MS/PhD in CS/EE

Tools

AWS

Azure

GCP

Job description

DatologyAI in San Mateo, CA, is seeking a Software Engineer Intern to join our Infrastructure team for Winter 2027 (January–April 2027). You will design and build systems that power large-scale data curation and model training, gaining hands-on experience with distributed systems and multi-cloud environments.

You’ll own meaningful projects, learn scale, reliability, and efficiency in AI workloads, and collaborate with senior engineers and researchers to push production-ready ML infrastructure